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Publications of Elena Zhizhina
Result of the query in the list of publications :
2 Technical and Research Reports |
1 - Object extraction using a stochastic birth-and-death dynamics in continuum.
X. Descombes and R. Minlos and E. Zhizhina. Research Report 6135, INRIA, 2007.
Keywords : birth and death process, Stochastic modeling, Wavelets.
@TECHREPORT{RR-6135,
|
| author |
= |
{Descombes, X. and Minlos, R. and Zhizhina, E.}, |
| title |
= |
{Object extraction using a stochastic birth-and-death dynamics in continuum}, |
| year |
= |
{2007}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6135}, |
| url |
= |
{https://hal.inria.fr/inria-00133726}, |
| pdf |
= |
{http://hal.inria.fr/inria-00133726}, |
| keyword |
= |
{birth and death process, Stochastic modeling, Wavelets} |
| } |
Abstract :
We define a new birth and death dynamics dealing with configurations of discs in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in extracting objects. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data. |
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2 - Image Denoising using Stochastic Differential Equations.
X. Descombes and E. Zhizhina. Research Report 4814, INRIA, France, May 2003.
Keywords : Denoising.
@TECHREPORT{4814,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{Image Denoising using Stochastic Differential Equations}, |
| year |
= |
{2003}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4814}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071772}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71772/filename/RR-4814.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/17/72/PS/RR-4814.ps}, |
| keyword |
= |
{Denoising} |
| } |
Résumé :
Ce rapport concerne le problème de la restauration d'image avec une approche par Équation Différentielle Stochastique. Nous considérons un processus de diffusion convergeant vers une mesure de Gibbs. L'hamiltonien de la mesure de Gibbs contient un terme d'interactions, apportant des contraintes de lissage sur la solution, et un terme d'attache aux données. Nous étudions deux schémas d'approximation discrète de la dynamique de Langevin associée à ce processus de diffusion : les approximation d'Euler et explicite forte de Taylor. La vitesse de convergence des algorithmes correspondants est comparée à celle de l'algorithme de Metropolis-Hasting. Des résultats sont montrés sur des images de synthèse et réelles. Il montrent la supériorité de l'approche proposée lorsque l'on considère un faible nombre d'itérations. |
Abstract :
We address the problem of image denoising using a Stochastic Differential Equation approach. We consider a diffusion process which converges to a Gibbs measure. The Hamiltonian of the Gibbs measure embeds an interaction term, providing smoothing properties, and a data term. We study two discrete approximations of the Langevin dynamics associated with this diffusion process: the Euler and the Explicit Strong Taylor approximations. We compare the convergence speed of the associated algorithms and the Metropolis-Hasting algorithm. Results are shown on synthetic and real data. They show that the proposed approach provides better results when considering a small number of iterations. |
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